This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Este curso forma parte de Programa Especializado - Ciencia de Datos
Reproducible Research
ofrecido por
Acerca de este Curso
4.5
2,958 calificaciones
Qué aprenderás
Determine the reproducibility of analysis project
Organize data analysis to help make it more reproducible
Publish reproducible web documents using Markdown
Write up a reproducible data analysis using knitr
Habilidades que obtendrás
KnitrData AnalysisR ProgrammingMarkup Language
Programa - Qué aprenderás en este curso
Semana
1Week 1: Concepts, Ideas, & Structure
This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.
9 videos (Total 72 minutos), 3 readings, 1 quiz
9 videos
What is Reproducible Research About?8m
Reproducible Research: Concepts and Ideas (part 1)7m
Reproducible Research: Concepts and Ideas (part 2) 5m
Reproducible Research: Concepts and Ideas (part 3) 3m
Scripting Your Analysis 4m
Structure of a Data Analysis (part 1)12m
Structure of a Data Analysis (part 2)17m
Organizing Your Analysis11m
3 lecturas
Syllabus10m
Pre-course survey10m
Course Book: Report Writing for Data Science in R10m
1 ejercicio de práctica
Week 1 Quiz20m
Semana
2Week 2: Markdown & knitr
This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.
9 videos (Total 59 minutos), 2 quizzes
9 videos
Markdown5m
R Markdown6m
R Markdown Demonstration7m
knitr (part 1)7m
knitr (part 2) 4m
knitr (part 3) 4m
knitr (part 4) 9m
Introduction to Course Project 14m
1 ejercicio de práctica
Week 2 Quiz10m
Semana
3Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.
10 videos (Total 60 minutos)
10 videos
RPubs 3m
Reproducible Research Checklist (part 1)8m
Reproducible Research Checklist (part 2) 10m
Reproducible Research Checklist (part 3) 6m
Evidence-based Data Analysis (part 1)3m
Evidence-based Data Analysis (part 2) 3m
Evidence-based Data Analysis (part 3) 4m
Evidence-based Data Analysis (part 4) 4m
Evidence-based Data Analysis (part 5) 7m
Semana
4Week 4: Case Studies & Commentaries
This week there are two
case studies involving the importance of reproducibility in science for you to watch.
5 videos (Total 59 minutos), 1 reading, 1 quiz
5 videos
Case Study: Air Pollution14m
Case Study: High Throughput Biology30m
Commentaries on Data Analysis2m
Introduction to Peer Assessment 232s
1 lectura
Post-Course Survey10m
33%
comenzó una nueva carrera después de completar estos cursos
31%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
por AA•Feb 13th 2016
My favorite course, at least it gives me an argument why scripted statistics is awesome and can be applied to a number of data related activities. Recycling chunks of code has proven useful to me.
por AS•Jun 23rd 2017
Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.
Instructores
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Acerca de Universidad Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Acerca del programa especializado Ciencia de Datos
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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